• Animal Systematics, Evolution and Diversity
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• v.36 no.4
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• pp.336-346
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• 2020

Mitochondrial genome is an important molecule for systematic and evolutionary studies in metazoans. The development of next-generation sequencing (NGS) technique has rapidly increased the number of mitogenome sequences. The process of generating mitochondrial genome based on NGS includes different steps, from DNA preparation, sequencing, assembly, and annotation. Despite the effort to improve sequencing, assembly, and annotation methods of mitogenome, the low quality and/or quantity sequence in the final map can still be generated through the work. Therefore, it is necessary to check and curate mitochondrial genome sequence after annotation for proofreading and feedback. In this study, we introduce the pipeline for sequencing and curation for mitogenome based on NGS. For this purpose, two mitogenome sequences of Dermatobranchus otome were sequenced by Illumina Miseq system with different amount of raw read data. Generated reads were targeted for assembly and annotation with commonly used programs. As abnormal repeat regions present in the mitogenomes after annotation, primers covering these regions were designed and conventional PCR followed by Sanger sequencing were performed to curate the mitogenome sequences. The obtained sequences were used to replace the abnormal region. Following the replacement, each mitochondrial genome was compared with the other as well as the sequences of close species available on the Genbank for confirmation. After curation, two mitogenomes of D. otome showed a typically circular molecule with 14,559 bp in size and contained 13 protein-coding genes, 22 tRNA genes, two rRNA genes. The phylogenetic tree revealed a close relationship between D. otome and Tritonia diomea. The finding of this study indicated the importance of caution and curation for the generation of mitogenome from NGS.

• The KIPS Transactions:PartB
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• v.9B no.5
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• pp.541-548
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• 2002

This paper proposes an XML based Multimedia Annotation and Retrieval System, which can represent and retrieve video data efficiently using XML. The system provides a graphical user interface for annotating, searching, and browsing multimedia data. It is Implemented based on the hierarchical metadata model to represent multimedia information. The metadata about video is organized based on multimedia description schema using XML Schema that basically conforms to the MPEG-7 standard. Also, for the effective indexing and retrieval of multimedia data, video segments are annotated and categorized using the closed caption.

• Journal of KIISE:Software and Applications
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• v.32 no.7
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• pp.650-662
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• 2005

A robust repositioning method is required for annotations to always maintain proper positions when original documents were modified. Robust anchoring in the XML document provides better anchoring results when it includes features of structured documents as well as annotated texts. This paper proposes robust annotation anchoring method in XML document. To do this, this work presents annotation information as logical structure trees, and creates candidate anchors by analyzing matching relations between the annotation and document trees. To select the appropriate candidate anchor among many candidate anchors, this work presents several anchoring criteria based on the textual and label context of anchor nodes in the logical structure trees. As a result, robust anchoring is realized even after various modifications of contexts in the structured document.

• Genomics & Informatics
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• v.10 no.1
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• pp.33-39
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• 2012

High-throughput genomic technologies (HGTs), including next-generation DNA sequencing (NGS), microarray, and serial analysis of gene expression (SAGE), have become effective experimental tools for cancer genomics to identify cancer-associated somatic genomic alterations and genes. The main hurdle in cancer genomics is to identify the real causative mutations or genes out of many candidates from an HGT-based cancer genomic analysis. One useful approach is to refer to known cancer genes and associated information. The list of known cancer genes can be used to determine candidates of cancer driver mutations, while cancer gene-related information, including gene expression, protein-protein interaction, and pathways, can be useful for scoring novel candidates. Some cancer gene or mutation databases exist for this purpose, but few specialized tools exist for an automated analysis of a long gene list from an HGT-based cancer genomic analysis. This report presents a new web-accessible bioinformatic tool, called CaGe, a cancer genome annotation system for the assessment of candidates of cancer genes from HGT-based cancer genomics. The tool provides users with information on cancer-related genes, mutations, pathways, and associated annotations through annotation and browsing functions. With this tool, researchers can classify their candidate genes from cancer genome studies into either previously reported or novel categories of cancer genes and gain insight into underlying carcinogenic mechanisms through a pathway analysis. We show the usefulness of CaGe by assessing its performance in annotating somatic mutations from a published small cell lung cancer study.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.3
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• pp.1071-1092
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• 2014

Annotating images with tags that describe the content of the images facilitates image retrieval. However, this task is challenging for both humans and computers. In response, a new approach has been proposed that converts the manual image annotation task into CAPTCHA challenges. However, this approach has not been widely used because of its weak security and the fact that it can be applied only to annotate for a specific type of attribute clearly separated into mutually exclusive categories (e.g., gender). In this paper, we propose a novel image annotation CAPTCHA scheme, which can successfully differentiate between humans and computers, annotate image content difficult to separate into mutually exclusive categories, and generate verified test images difficult for computers to identify but easy for humans. To test its feasibility, we applied our scheme to annotate images of human faces with their age groups and conducted user studies. The results showed that our proposed system, called AgeCAPTCHA, annotated images of human faces with high reliability, yet the process was completed by the subjects quickly and accurately enough for practical use. As a result, we have not only verified the effectiveness of our scheme but also increased the applicability of image annotation CAPTCHAs.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.1
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• pp.213-231
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• 2014

Existing image search systems allow users to search images by keywords, or by example images through content-based image retrieval (CBIR). On the other hand, users might learn more relevant textual information about an image from its text captions or surrounding contexts within documents or Web pages. Without such contexts, it's difficult to extract semantic description directly from the image content. In this paper, we propose an annotation watermarking system for users to embed text descriptions, and retrieve more relevant textual information from similar images. First, tags associated with an image are converted by two-dimensional code and embedded into the image by discrete wavelet transform (DWT). Next, for images without annotations, similar images can be obtained by CBIR techniques and embedded annotations can be extracted. Specifically, we use global features such as color ratios and dominant sub-image colors for preliminary filtering. Then, local features such as Scale-Invariant Feature Transform (SIFT) descriptors are extracted for similarity matching. This design can achieve good effectiveness with reasonable processing time in practical systems. Our experimental results showed good accuracy in retrieving similar images and extracting relevant tags from similar images.

• Genomics & Informatics
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• v.19 no.3
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• pp.23.1-23.7
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• 2021

Currently, coronavirus disease 2019 (COVID-19) literature has been increasing dramatically, and the increased text amount make it possible to perform large scale text mining and knowledge discovery. Therefore, curation of these texts becomes a crucial issue for Bio-medical Natural Language Processing (BioNLP) community, so as to retrieve the important information about the mechanism of COVID-19. PubAnnotation is an aligned annotation system which provides an efficient platform for biological curators to upload their annotations or merge other external annotations. Inspired by the integration among multiple useful COVID-19 annotations, we merged three annotations resources to LitCovid data set, and constructed a cross-annotated corpus, LitCovid-AGAC. This corpus consists of 12 labels including Mutation, Species, Gene, Disease from PubTator, GO, CHEBI from OGER, Var, MPA, CPA, NegReg, PosReg, Reg from AGAC, upon 50,018 COVID-19 abstracts in LitCovid. Contain sufficient abundant information being possible to unveil the hidden knowledge in the pathological mechanism of COVID-19.

• Genomics & Informatics
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• v.19 no.3
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• pp.21.1-21.5
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• 2021

The use of social media data, like Twitter, for biomedical research has been gradually increasing over the years. With the coronavirus disease 2019 (COVID-19) pandemic, researchers have turned to more non-traditional sources of clinical data to characterize the disease in near-real time, study the societal implications of interventions, as well as the sequelae that recovered COVID-19 cases present. However, manually curated social media datasets are difficult to come by due to the expensive costs of manual annotation and the efforts needed to identify the correct texts. When datasets are available, they are usually very small and their annotations don't generalize well over time or to larger sets of documents. As part of the 2021 Biomedical Linked Annotation Hackathon, we release our dataset of over 120 million automatically annotated tweets for biomedical research purposes. Incorporating best-practices, we identify tweets with potentially high clinical relevance. We evaluated our work by comparing several SpaCy-based annotation frameworks against a manually annotated gold-standard dataset. Selecting the best method to use for automatic annotation, we then annotated 120 million tweets and released them publicly for future downstream usage within the biomedical domain.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.11a
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• pp.162-164
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• 2019

본 논문에서는 물체인식 딥러닝 모델 생성에 필요한 라벨링(Labeling)과정에서 사용자가 다양한 기능을 활용하여 효과적인 학습 데이터를 구성할 수 있는 GUI 프로그램을 구현했다. 프로그램의 인터페이스는 파이썬 기반의 GUI 모듈인 Tkinter 를 활용하여, 실시간으로 이미지 데이터를 수집할 수 있는 크롤링(Crawling)기능과 미리 학습된 Retinanet 을 통해 이미지 데이터를 인식함으로써 자동으로 주석(Annotation) 과정을 수행할 수 있는 기능을 구성했다. 또한, 수집한 이미지 데이터를 다양한 효과와 노이즈, 변형 등으로 Augmentation 기능을 추가함으로써, 사용자가 모델을 학습하기 위한 데이터 전처리 단계를 하나의 GUI 프로그램에서 수행할 수 있도록 했다. 또한 사용자가 직접 학습한 모델을 추정 모델(Inference Model)로 변환하여 프로그램에 입력할 수 있도록 설계한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.11a
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• pp.273-276
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• 2006

본 논문에서는 일반적인 수족관에서 물고기를 관찰하는데 있어서 관찰자가 빠르고 효율적으로 물고기에 대한 정보를 얻을 수 있는 방법을 제안하고자 한다. 여러 종의 물고기가 살고 있는 수족관 환경에서 처음 보는 관찰자과 쉽게 물고기에 대한 정보를 얻기는 힘들다. 이러한 제약을 효과적으로 개선하기 위하여 반 투영 거울을 사용하는 공간 증강 현실을 위한 영상출력 기법을 이용한다. 이때 피사체의 위치 정보를 얻기 위해 배경인 수족관영상과 피사체인 물고기영상을 분리한 후 물고기의 깊이 정보 값을 추출하기 위하여 호모그래피 행렬을 이용하고 보정된 이미지를 계산 한다. 실험결과 본 논문에서 제안한 방법을 통해 구축한 이 시스템으로 관찰자는 각각의 물고기들에 대한 정보를 반응형 Annotation으로 쉽게 얻을 수 있었다.